Stephen Wolfram: How AI Works and How to Use It to Stay Ahead | Artificial Intelligence | AI Vault

Most people have been using AI for decades, but only a few understand how to leverage it. After more than 40 years in the field, Stephen Wolfram has seen how breakthroughs like ChatGPT seem to emerge out of nowhere, and he believes the real power isn’t the technology itself, but learning how to think in a way machines can work with. Now on Spotify Video! In this episode of the AI Vault series, Stephen breaks down how artificial intelligence truly works, what the future of automation will look like, and why mastering computational thinking is the next critical skill for entrepreneurs and innovators. In this episode, Hala and Stephen will discuss: (00:00) Introduction (02:31) His Early Fascination With Science and AI (05:52) How Artificial Intelligence Began (14:18) The Foundations of Computational Thinking (21:31) The Role of Computational Thinking in AI (25:52) How ChatGPT and Neural Networks Work (33:45) Can AI Develop Real Consciousness? (39:23) How AI Will Transform the Future of Work (45:27) Will AI in Action Surpass Human Intelligence? Stephen Wolfram is a computer scientist, mathematician, theoretical physicist, and the founder and CEO of Wolfram Research. He created Mathematica, Wolfram Alpha, and the Wolfram Language, and is widely recognized for his pioneering work in computation and complex systems. A MacArthur “Genius” Grant recipient, Stephen has authored several influential books, including What Is ChatGPT Doing? Today, he stands as one of the leading voices shaping global understanding of AI and computational thinking. Sponsored By: Indeed - Get a $75 sponsored job credit to boost your job's visibility at Indeed.com/PROFITING Shopify - Start your $1/month trial at Shopify.com/profiting. Revolve - Head to REVOLVE.com/PROFITING and take 15% off your first order with code PROFITING DeleteMe - Remove your personal data online. Get 20% off DeleteMe consumer plans at to joindeleteme.com/profiting Spectrum Business - Visit Spectrum.com/FreeForLife to learn how you can get Business Internet Free Forever. Airbnb - Find yourself a cohost at airbnb.com/host Northwest Registered Agent - Build your brand and get your complete business identity in just 10 clicks and 10 minutes at northwestregisteredagent.com/paidyap Framer - Publish beautiful and production-ready websites. Go to Framer.com/design and use code PROFITING Intuit QuickBooks - Bring your money and your books together in one platform at QuickBooks.com/money Resources Mentioned: Stephen's Book, What Is ChatGPT Doing?: bit.ly/-ChatGPT Stephen’s Website: stephenwolfram.com Stephen's Book, A New Kind of Science: bit.ly/NKScience Stephen's Book, An Elementary Introduction to the Wolfram Language: bit.ly/WolframL Active Deals - youngandprofiting.com/deals Key YAP Links Reviews - ratethispodcast.com/yap YouTube - youtube.com/c/YoungandProfiting Newsletter - youngandprofiting.co/newsletter LinkedIn - linkedin.com/in/htaha/ Instagram - instagram.com/yapwithhala/ Social + Podcast Services: yapmedia.com Transcripts - youngandprofiting.com/episodes-new Entrepreneurship, Entrepreneurship Podcast, Business, Business Podcast, Self Improvement, Self-Improvement, Personal Development, Starting a Business, Strategy, Investing, Sales, Selling, Psychology, Productivity, Entrepreneurs, AI, Artificial Intelligence, Technology, Marketing, Negotiation, Money, Finance, Side Hustle, Startup, Mental Health, Career, Leadership, Mindset, Health, Growth Mindset, AI Marketing, Prompt, AI in Business, Generative AI, AI for Entrepreneurs, AI Podcast

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• 0:00Hala Taha?

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• 0:45Hala Taha?

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• 1:08Speaker 01

  AI is another step in the automation of things. This is the coming paradigm of the 21st century.

• 1:15Hala Taha?

  Dr. Stephen Wolfram. He's an award-winning, renowned computer scientist, mathematician, a theoretical physicist, and the founder of Wolfram Research.

• 1:24Speaker 01

  More and more systems in the world will get automated. When things get automated, things humans used to have to do with their own hands, they don't have to do anymore. Of all the things that are out there to compute, the set that we humans have cared about so far in the developments of our civilization is a tiny, tiny, tiny slice. The AI left to its own devices is an infinite set of things that it could be doing. The question is...

• 1:47Hala Taha?

  AI potentially being sentient or having any sort of agency? How does this make you feel about human consciousness?

• 1:54Speaker 01

  The thing that we learned from the advance of AI is, well, actually, there's not as much distance between the amazing stuff of our minds and things that are just able to be constructed computationally. I mean, one of the things to realize is...

• 2:08Hala Taha?

  Hello, young and profiteers. If you've been following along, you already know our AI Vault series is well underway, and it's been filled with insights from the greatest minds in artificial intelligence. In the last episode, we featured the godmother of AI, Dr. Fei-Fei Li, and we talked about human-centered AI. And this week, we're featuring Stephen Wolfram. Stephen is the founder of Wolfram Research, the creator of Mathematica and Wolfram Alpha, and he's one of the greatest scientific thinkers of our time when it comes to computational science and the nature of intelligence. If you've ever wondered what's really happening inside ChatGPT, or how AI systems actually think, this is a conversation you won't want to miss. Stephen will break down the mechanics of neural networks, large language models,

• 2:52Hala Taha?

  and his groundbreaking concept of computational thinking, a powerful way to solve problems that could transform not just technology, but the way we approach life and work itself. We'll cover the future of AI, its impact on jobs, creativity, and society, and why intelligence may be far bigger and more universal than we've ever imagined. And guys, if you're new here, don't just listen, make sure you subscribe so you can stay plugged into all these different AI conversations coming up. Without further ado, here's my conversation with Stephen Wolfram. Stephen, welcome to Young and Profiting Podcast.

• 3:25Speaker 01

  Hello there.

• 3:26Hala Taha?

  Hi, I am so excited for today's interview. We love the topic of AI, and I wanted to talk a little bit about your childhood before we got into the meat and potatoes of today's interview. So from my understanding, you started as a particle physicist at a very young age. You even started publishing scholarly papers as young as 15 years old. So talk to us about how you first got interested in science, and what you were like as a kid.

• 3:50Speaker 01

  Well, let's see. I grew up in England in the 1960s, when space was the thing of the future, which it is again now, but wasn't for 50 years. And so I was interested in those kinds of things, and that got me interested in kind of how things like spacecraft work, and that got me interested in physics. And so I started learning about physics and so happened that the early nineteen seventies were a time when lots of things were happening in particle physics, lots of new particles getting discovered, lots of fast progress and so on. And so I got involved in that. It's always cool to be involved in fields that are in their kind of golden age of expansion, which particle physics was at the time. So that was how I got into these things. You

• 4:34Speaker 01

  know, it's funny, you mentioned AI. And I realized that when I was a kid, sort of machines that think were right around the corner, just as, you know, colonization of Mars was right around the corner too. But it's kind of an interesting thing to see what actually happens over a fifty year span and what doesn't. And we're

• 4:55Hala Taha?

  It's so

• 4:56Speaker 01

  Go ahead.

• 4:57Hala Taha?

  I was gonna say, yeah, it's so crazy to think how much has changed over the last fifty years.

• 5:01Speaker 01

  And how much has not. I mean, I, you know, I've been in science, for example, I have just been finishing some projects that I started basically fifty years ago. And it's kind of cool to finish something that one, you know, there's a big science question that I started asking when I was twelve years old about how a thing that people have studied for a hundred and fifty years now works, the second law of thermodynamics. And I was sort of kind of interested in that when I was twelve years old. And I finally, I think, figured that out. I published a book about it last year. And it's kind of, kind of, kind of nice to see that one can tie up these things. But it's also a little bit shocking

• 5:43Speaker 01

  how slowly kind of big ideas move. I mean, for example, the neural nets that everybody's so excited about now in AI, neural nets were invented in nineteen forty-three. And the the original kind of conception of them is not that different from what people use today, except that now we have computers that run billions of times faster than things that were imagined back in the nineteen fifties and so on. But it's, it's, it is both, it's interesting, some things, occasionally things happen very quickly. Oftentimes, it's shocking how slow things, how slowly things happen, and how long it takes for the world to kind

• 6:24Speaker 01

  of absorb ideas. And, you know, sometimes there'll be an idea, and finally, some technology will make it possible to execute that idea in a way that wasn't there before. Sometimes there's an idea and it's been hanging out for a long time and people just ignored it for one reason or another. And I think, you know, some of the things that are happening with AI today probably could have happened a bit earlier. The, the, some things have depended on sort of the building of a, of a big technology stack. But it's, it's always interesting to see that, to me at least.

• 6:58Hala Taha?

  It's so fascinating and this actually dovetails perfectly into my next question is about your first experiences with AI. So now everybody knows what AI is, but really most of us really started to understand it and, and using this term maybe five years ago, max. But you've been kind of studying this for decades, even before people probably called it AI. So can you talk to us about the beginnings?

• 7:22Speaker 01

  No, AI predates me. AI was that, that, that term was invented in 1956. I, I know the couple of, I, I knew that they're dead now, but the couple of characters who, who invented that term. It's, you know, it's funny because the idea that we're going to, as soon as computers were invented, so electronic computers invented basically in the late 1940s and they started to become sort of things that people had seen by the beginning of the 1960s. Like I, I probably, I first saw a computer when I was 10 years old, which

• 8:01Speaker 01

  was 1969 ish. And the time, you know, a computer was a very big thing, tended by people in white coats and so on. And it's, and I first sort of got my hands on a computer in 1972. And that was a computer that was kind of the size of a large desk and programmed with paper tape and so on, and was, was rather primitive by today's standards. But kind of the elements were all there by that time. But most people had, it's true, most people had not seen a computer until probably the beginning of the 1980s or something. And which was when PCs and things like that started, started to come out. But, you know, it was

• 8:45Speaker 01

  from the very first moments when electronic computers came on the scene, people sort of assumed that computers would automate thought. As bulldozers and things like forklift trucks had automated kind of mechanical uh work. And you know, that was the, you know, the the giant electronic brains was a typical characterization of computers in the nineteen fifties. So this idea that, you know, one would automate thought was a very early idea. That was, that was um. Now, the question was, how easy, how hard was it going to be to do that? And people in the nineteen fifties and uh beginning of the nineteen sixties, they were like, this is going to be easy. You know, now we have these computers, it's going to be easy to replicate what brains do.

• 9:29Speaker 01

  In fact, a good example back in the beginning of the nineteen sixties, a famous incident was uh there was, it was during the Cold War and people were worried about, you know, US, Russian, Soviet communication and so on. They said, well, you know, maybe the people are in a room, there's some interpreter, the interpreter is going to not translate things correctly, so let's not use a human interpreter, let's teach a machine to do that translation, beginning of the nineteen sixties. And of course, machine translation, which is now finally in the twenty twenties, pretty good, took an extra, you know, sixty years to actually uh uh to actually happen. And people just didn't have the intuition about what was going to be hard, what wasn't going to be hard.

• 10:11Speaker 01

  So the, the kind of, the, the term AI was sort of in the air already very much by the, by the nineteen sixties. I'm sure, when I was a kid, I'm sure I read, you know, sort of uh books about the future in which AI was a thing, and it was certainly in, in movies and, and things like that. I think then, this question of, okay, so how would we get computers to do sort of thinking like things? You know, when I was a kid, I was interested in kind of taking sort of the, the knowledge of the world and somehow cataloging it and so on. I don't know why I got interested in that, but that's something I've been interested in for a long time. And so I started thinking, you know, how would we take kind of the knowledge of the

• 10:52Speaker 01

  world and make it automatic to be able to answer questions based on sort of the knowledge that our civilizations accumulated? And uh by the time, uh so I, I started building things along those lines and I, I started building whole technology stack that I'd started in the late nineteen seventies and, and um uh well, now it's turned into a big thing that lots of people use, but um. That kind of the, the idea there, the first idea there was, let's be able to compute things like math and so on, and let's, let's sort of take what has been something that humans have to do and make it automatic to have computers do it. So that was a thing when it, when it came to, for example, people had said for a while,

• 11:35Speaker 01

  when computers can do calculus, then we'll know that they're intelligent. Okay, so, you know, things I built solved that problem. By the, by the mid nineteen eighties, that problem was pretty well solved. And then people said, well, it's just engineering. It's not really a computer sort of being intelligent. I would agree with that. But then, actually, at the, at the very beginning of the nineteen eighties, when I was working on kind of automating things like mathematical computation, I got curious about sort of the more general problem of sort of doing the kinds of things that we humans do, like we match patterns, we say, that's a picture, you know, we see this, this image, and it's got a bunch of pixels in it, and we say, that's a picture of a cat, or that's a picture of a dog.

• 12:18Speaker 01

  And this question of how do we do that kind of pattern matching, I got interested in, and started trying to figure out how to make that work. I knew about neural nets. I started trying to get, this must have been nineteen eighty, eighty-one, something like that, I started trying to get neural nets to do things like that, but they didn't work at all at the time. Hopeless. They're just, actually, as, as it turns out, in a, in a bizarre, you know, you say things happen quickly, and I say things sometimes happen very slowly. Um, the, uh, uh, I was just working on something that is kind of a potential new direction for how neural nets and things like that might work. And I realized, you know, I did this once, I worked on this once before, and I pulled

• 13:01Speaker 01

  out this paper that I wrote in nineteen eighty-five, that has kind of this same basic idea that I was just very proud of myself for having figured out just last week. And it's like, well, I started on it in nineteen eighty-five, well, now, you know, I understand a bunch more, and we have much more powerful computers, maybe I can make this idea work. But so. The um, this, this notion that uh, one could do. There, there are things that people thought would be hard for computers, like doing calculus and so on. Uh, you know, we crushed that, so to speak, a long time ago. Hmm. Then there were things that are super easy for people, like tell that's a cat, that's a dog. Which wasn't solved, and I wasn't involved in the solving of that.

• 13:46Speaker 01

  That's something that um, uh, people worked on for a long time and, and nobody thought it was gonna work. And then suddenly in twenty eleven, sort of through a mistake. Some people who'd been working on this for a long time. Kind of left a computer trying to train to tell things like cats from dogs. For a month without paying attention to it, they came back. They didn't think anything exciting would have happened, and by golly, it had worked. And that's what kind of started the, the, the current enthusiasm about neural nets and deep learning and so on. And uh, you know, when, when ChatGPT came out in, in late twenty twenty two. Again, the people who'd been working on it, they didn't know it was gonna work. There had been previous, you know, we had worked on. Previous kinds of language models, things that tried to uh.

• 14:31Speaker 01

  Do things like predict what the next word will be in a sentence, those sorts of things. And they were really pretty crummy. And suddenly, uh, for reasons that we still don't understand. We kind of got above this threshold, where it's like, yes, this is pretty human-like. Um, and uh, it's not clear what caused that threshold, it's not clear whether. You know, we, in our human languages, for example, we might have, I don't know, forty thousand words that are common in a language like. Hmm. Most languages, English as an example. And um, it's kind of, you know, that, there's probably. That number of words is somehow related to how big a, an artificial brain you need to be able to deal with language in a reasonable way.

• 15:13Speaker 01

  And you know, if our brains were bigger, maybe we would routinely have languages with two hundred thousand words in them. We don't know. Hmm. And maybe, uh, you know, it's this kind of match between what, what we can. Do with an artificial neural network versus what our human sort of biological neural nets manage to do. We've managed to reach enough of a match that people say, by golly, the thing seems to be doing. Uh, the kinds of things that, that, uh, that we humans do. But that's um. But I mean, this question, what's ended up happening is there this this sort of what us humans can quickly do, like tell a cat from a dog, or figure out what the next word in the sentence is likely to be.

• 15:58Speaker 01

  Then there are things that we humans have actually found really hard to do, like solve this math problem, or figure out this thing in science, or do this kind of simulation of what happens in the natural world. Those are things that with sort of the unaided brain doesn't manage to do very well on. But, you know, the big thing that's happened last 300 years or so is we built a bunch of kind of formalization of the world with first with things like logic, that was back in antiquity, and then with math, and most recently with computation, where we're kind of setting up things so that we can talk about things in a more structured way than just the way that we think about them sort of off the top of our head, so to speak.

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• 19:58Hala Taha?

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• 22:07Hala Taha?

  And listeners of this show will get a $75 sponsored job credit to get your jobs more visibility at indeed.com/profiting. Just go to indeed.com/profiting right now and support our show by saying you heard about Indeed on this podcast. Indeed.com/profiting. Terms and conditions apply. Hiring Indeed is all you need. So that's so interesting. And I know that you work on something called computational thinking. And I think what you're saying now really relates to that. So help us understand the Wolfram project and computational thinking and how it's related to the fact that humans, we need to formalize and organize things, like you said, like mathematics and logic. Why do we, like, what's the history behind that? Why do we need to do that as humans? And then how does it relate to computational thinking in the future?

• 22:51Speaker 01

  Well, so there's, there's kind of things one can immediately figure out. One just sort of intuitively knows, oh, that's a cat, that's a dog, whatever. Then there are things where you have to kind of go through a process of working out what's true. And or working out sort of how to construct this or that thing. When you're going through that process, you've got to have kind of solid bricks to start building that tower. And it's not something, so, so you have to, so what are those bricks going to be made of? Well, you have to have something which has sort of definitive structure. And that's something where, for example, back in antiquity, when logic got invented, it was kind of like, well, you can think vaguely, oh, that sentence sounds kind of right.

• 23:35Speaker 01

  Or you can say, well, wait a minute, you know, this or that, if one of those things is true, then the that or that has to be true, et cetera, et cetera, et cetera. You've got some structured way to think about things. And then in 1600s, you know, math became sort of a popular way to think about the world. And then you could say, okay, we're looking at, well, roughly, you know, the a planet goes around the sun and roughly an ellipse, but let's, let's put math into that. And then we can have this kind of way to actually compute what's going to happen. And then. So for about three hundred years, kind of this, this idea of math is going to explain how the world works at some level was kind of a dominant theme.

• 24:20Speaker 01

  And that worked pretty well in physics. It worked pretty terribly in things like biology, in social sciences and so on. You know, people imagined there might be a social physics of how society works. That never really panned out. Um, same, so, so there was sort of this question of things that, the things were, places where math had worked and it gave us a lot of modern engineering and so on, and the cases where it hadn't really worked. I got pretty interested in this at the beginning of the nineteen eighties and sort of figuring out, how do you sort of formalize thinking about the world in a way that goes beyond what math provides one? Things like, you know, calculus and so on, give one. Uh, what I realized is that, that you kind of, you just think about, well, there are

• 25:05Speaker 01

  definite rules that describe how things work and those rules are more stated in terms of, oh, you have this arrangement of black and white cells and then this happens and so on. They're not things that you necessarily can write in mathematical terms, in terms of multiplications and integrals and, and things like this. And so I, as a matter of science, I kind of got interested in, so what do these simple programs that you can describe as, these kind of systems as rules of being, what do they typically do? And kind of what one might have assumed is, you have a program that's simple enough, it's going to just do simple things. This turns out not to be true. Big surprise, to me at least. I think to everybody else as well. Took, took people a few decades to kind of absorb this point.

• 25:50Speaker 01

  It took me a, a solid bunch of years to absorb this point. But, you know, I, you just do these experiments, computer experiments, and you find out, yes, you use a simple rule and no, it does a complicated thing. And that's, that turns out to be pretty interesting if you want to understand how nature works, because it seems like that's kind of, it's kind of the secret that nature uses to make a lot of the complicated stuff that we see, the same phenomenon of simple rules, complicated behavior. So that turns into a whole big direction and, and kind of new understanding about how science works. I wrote this big book back in two thousand two called The New Kind of Science, which is, uh, well, its title kind of says what it is. Um, it, it's some-

• 26:35Speaker 01

  That that was uh uh so that's one kind of branch is sort of understanding the world in terms of sort of computational rules. Another thing has to do with taking the things that we normally think about. Whether that's, you know, how long is going to take, how far is it from one city to another? Or, you know, how does, how do we, you know, make this image have this, this uh? How do we remove, you know, this thing from this image or something like this? Things that we would normally think about and talk about. And how do we take those kinds of things? And think about them in a structured computational way? And so that, that, that has turned into a big enterprise in my life, which is

• 27:18Speaker 01

  building our computational language, this thing now called Wolfram Language, that um uh powers a lot of kinds of, well, research and development kinds of things, and also lots of actual practical systems in the world. Although when you are interacting with those systems, you don't see what's, what's inside them, so to speak. But um kind of the idea there is to make a, a language for describing things in the world, which might be, you know, this is a, a city. This is uh, you know, you might say, what's uh, it, it, it's, it's both a uh, the concept of a city and the actuality of the couple of hundred thousand cities that exist in the world,

• 28:02Speaker 01

  where they are, what their populations are, lots of other data about them. And being able to compute uh uh things about things in the world, so to speak. And so that's been sort of a big effort to build up that computational language. And, and the thing that's, that's kind of exciting that we're sort of on the cusp of, I suppose, is, you know, people who study things like science and so on, for the last three hundred years, it's like, okay, you're, you know, to make this science really work, you have to make it somehow mathematical. Well, the thing that's sort of now. The case is that the sort of the new way to make science really work is to make it computational. And so you see all these different fields, you know, call them X.

• 28:47Speaker 01

  You start seeing the computational X field start to come into existence. And kind of my, I suppose, one of my big life missions has been to provide the sort of language and notation for making computational X for all X possible. It's kind of a similar mission to what people did maybe 500 years ago when people invented mathematical notation. I mean, there was a time when if you wanted to talk about math, it was all in terms of just regular words at the time in Latin. But the, and then people invented things like plus signs and equal signs and so on. And that kind of streamlined the way of talking about math. And that's what led to, for example, algebra and then calculus and then all the kind of modern mathematical science that we have.

• 29:33Speaker 01

  And so similarly, what I've been trying to do last 40 years or so is is build kind of a computational language, a notation for computation, a way of talking about things computationally that lets one sort of build computational X for all X. And one of, one of the great things that happens when you make things computational is not only do you have a clearer way to describe what you're talking about, but also your computer can help you figure it out, so to speak. And so you, you get this kind of this, this sort of superpower. As soon as you can express yourself computationally, you kind of tap into the superpower of actually being able to compute things. And that's, that's amazingly powerful.

• 30:15Speaker 01

  And, you know, when, when, when I was a kid, as I say, in the 1970s and, you know, physics was sort of hopping at the time because various new methods have been invented, not related to computers. At this time, sort of all the computational X fields are just starting to kind of really, really hop. And it's starting to be possible to do, to do really, really interesting things. And that's going to be sort of the, an area of tremendous growth in the next how many years.

• 30:45Hala Taha?

  It sounds really, really exciting. So I have a few follow-up questions to that. So you say that computational thinking is another layer in human evolution. So I want to understand why you feel it's going to help humans evolve. Also curious to understand like the practical ways that you're using the Wolfram language and how it relates to AI, if it does at all.

• 31:07Speaker 01

  Yeah, yeah, right. Well, so let's take the second thing first, okay? So Wolfram Language is about representing the world computationally in a sort of precise computational way. It also happens to make use of a bunch of AI, but let's put that aside. The way that, for example, something like an LLM, like a ChatGPT or something like that, it's, what it does is it makes up pieces of language. So you know, if we have a sentence like, you know, "The cat sat on the..." blank. What it will have done is it's read a billion webpages.

• 31:49Speaker 01

  Chances are, the most common next word is going to be "mat". And it has kind of set itself up so that it kind of knows that the most common next word is "mat", so let's write down "mat". So the big surprise is that it doesn't just do simple things like that, but having kind of built the structure from kind of reading all these webpages, it can write sort of plausible sentences. Those sentences might make, they sort of sound like they make sense. They're kind of typical of what you might read. They might or might not actually have anything to do with reality in the world, so to speak.

• 32:28Speaker 01

  And so, but that's working kind of the way humans immediately think about things. Then there's the separate whole idea of kind of formalized knowledge, which is the thing that led to kind of modern science and so on. That's a different branch from kind of things humans just can quickly and naturally do. So in a sense, Wolfram Language, the big contribution right now to sort of the world- Of of the emerging kind of AI language models, all this kind of thing. Is that we have this computational view of the world, which allows one to do precise computations and build up these kind of whole towers of consequences. So the typical setup and you'll see more more and more coming out along these lines.

• 33:13Speaker 01

  Um, I mean, we built something with OpenAI back, back, oh gosh, a year ago now. Uh, that was sort of a, an early version of this is you've got the, the language model and it's trying to make up words. And then it gets to use as a tool, our computational language. If it can formulate what it's talking about, well, you know, we have ways to take the natural language that it produces. We've had a Wolfman Alpha system, which came out in two thousand nine. Is a system that has natural language understanding. We sort of had solved the problem of small sentence, you know, one sentence at a time. Kind of, uh, what does this mean? Can we translate this natural language in English, for example,

• 33:55Speaker 01

  into computational language, then compute an answer using potentially many, many steps of computation. Um, and then, then that's something that is sort of a solid answer that was computed from knowledge that we've curated, et cetera, et cetera, et cetera. So kind of the, the, the typical mode of interaction is there's sort of a linguistic interface provided by things like L L M's. And that, that using our computational language as a tool to actually figure out, hey, this is the thing that's actually true, so to speak. And that's a, that's a mechanism. It's sort of, uh, it's the, just as humans don't necessarily immediately know everything.

• 34:36Speaker 01

  They, but with tools, they can get a long way. I suppose it's been sort of the story of my life, at least is, uh, you know, I, I discovered computers as a tool back in nineteen seventy two. And I've been using them ever since and managed to figure out, well, a number of interesting things in science and technology and so on. By, by using this kind of, uh, uh, external to me, kind of superpower tool of computation, so to speak. And that's some, uh, and it's kind of, well, the, the, the L L M's and the A I's get to do the same thing. So that's kind of the, the, how we sort of the, the, is this, that's sort of the, the core part of, um, the, uh, How the technology I've been building for a long time kind of most immediately fits

• 35:20Speaker 01

  into the the current sort of expansion of excitement about about AI and language models and so on. I think the I mean, there are there are other pieces to this which have to do with how, for example, science that I've done relates to understanding more about how you can build other kinds of AI like things, but that's that's sort of a separate branch.

• 35:44Hala Taha?

  Yeah, well, this is so fascinating. Honestly, you're teaching us so much. I feel like a lot of people tuning in are probably learning a lot of this stuff for the first time. But one thing that we all are using right now is ChatGPT, right? So everybody has sort of embraced ChatGPT. It kind of feels like it's magic, right? When you're just getting something that is giving you something that a human could potentially write. So I have a couple of questions about ChatGPT. You alluded to how it works a bit, but can you give us more detail about how neural networks work in general and what ChatGPT is doing in the background to spit out something that looks like it's written by a human?

• 36:22Speaker 01

  Yeah, so I mean, neural networks. So so, you know, the original inspiration for neural networks was understanding something about how brains work. You know, in our brains, we have about roughly 100 billion neurons. Each neuron is a little electrical device, so to speak, and they're kind of connected with things that kind of look under a microscope a bit like wires. So one neuron might be connected to 1,000 or 10,000 other neurons in one's brain. And these neurons kind of they'll have a little electrical signal and then they'll pass on that electrical signal to another neuron. And pretty soon, you know, there's one's gone through a whole chain of neurons and one says the word, next word

• 37:02Speaker 01

  or whatever. The and so this kind of the electrical machine, kind of lots of things connected to things. That was that's kind of how people imagine that brains work. And and that's how neural nets are kind of an idealization of that. Uh, set up in a computer where what one has these um. Uh, these connections between sort of artificial neurons, usually called weights. You often hear about people saying, you know, uh, this this thing has a trillion weights or something. Those are those are kind of the the connections between artificial neurons. And each one has a number associated with it. And so what happens when, you know, when you ask chat G P T something?

• 37:46Speaker 01

  What will happen is it will take the words that it's seen so far, the prompt. And it will grind them up into numbers. And it will take that sequence of numbers and feed that in as input to this network. So it's just it's just saying here it it takes the words every more or less every word in English gets a number. Or every part of a word gets a number. You have the sequence of numbers. That sequence of numbers is is given as input to this essentially mathematical computation. That goes through and says, okay, here's here's this arrangement of numbers. We multiply each number by this weight, then we add up a bunch of numbers. Then we take sort of a threshold of those numbers and so on.

• 38:29Speaker 01

  And we keep doing this and we do it a sequence of times, like a few hundred times for for typical kind of. Chat G P T type type type behavior. Few hundred times and then at the end we get another number. Actually, we got another collection of numbers that represent the probabilities. That the next word should be this or that. So in the example of the cat sat on the. The next word has probably very high probability, ninety nine percent probability to be mat. And, uh, you know, one percent probability or a point five percent probability to be floor or something. And then what? What ChatGPT is doing is it's saying, well, usually I'm going to pick the most likely next word.

• 39:11Speaker 01

  Sometimes I'll pick a word that isn't the absolutely most likely next word. And it just keeps doing that. So, and the surprise is that just doing that kind of thing, a word at a time, gives you something that seems like a reasonable English sentence. Now, the next question is, well, how did it figure out all those weights? That, how did it get all those, all those, in the case of the original ChatGPT, I think it was a hundred and eighty billion weights? Um, how did it, how did it get those numbers? And the answer is, what, what it tried to do was, it was trained, and it was trained by being shown all this text from the web. And what was happening was, was like, well, you've got one arrangement of weights.

• 39:56Speaker 01

  Okay, what, what next word does that predict? Okay, that predicts turtle as the next word for the cat sat on the. Turtle is wrong, let's change that. Let's see what happens if we, you know, adjust these weights in that way. Oh, we finally got it to say mat. Great, that's the correct version of that particular weight. Well, you keep doing that over and over again, that's a, it takes huge amounts of computer effort. You keep on bashing it and trying to get it, no, no, no, you got it wrong. You know, adjust it slightly to make it closer to correct. Keep doing that long enough, and you got something which is a neural net, which has the property that it will typically reproduce the kinds of things it's seen. Now, it's not enough to reproduce what it's seen,

• 40:41Speaker 01

  because if you keep going, writing a big, long essay, a lot of what's in that essay will never have been seen before. It will, it will be something that's, those particular combination of words will never have been produced before. So then the question is, well, how does it extrapolate? How does it figure out something that it's never seen before? What words is it going to use when it never saw it before? And this is the thing which nobody knew what was going to happen. It's, this is the thing where the big surprise is that the way it extrapolates is similar to the way we humans seem to extrapolate things. And presumably, that's because its structure is similar to the structure of our brains. We don't really know why it sort of when it, when it figures things out that it, that it hasn't seen before, why it does that in a kind of human-like way.

• 41:26Speaker 01

  That's kind of a, a scientific discovery. Now, we can say, you know, can we get an idea why this might happen? I think we have an idea why it might happen. And it's more or less this, that you say, how do you put together an English sentence? Well, you kind of learn basic grammar, you say it's a noun, a verb, a noun, that's a sort of typical English sentence. But there are many noun, verb, noun, English sentences that aren't really reasonable sentences, like, I don't know. Uh, uh, the electron ate the moon. Okay, it's grammatically correct, but it probably doesn't really mean anything, except in some kind of poetic sense. So the question then is, then what you realize is there's a more elaborate construction kit

• 42:10Speaker 01

  about sentences that might mean something. And people have been, have been intending to kind of create that construction kit for a couple of thousand years. I mean, Aristotle started, the time when he created logic, he started thinking about that kind of construction kit, but nobody got around to doing it. And, but I think, you know, chat G P T and, and L L M's kind of show us there is a construction kit of, oh, you know, that word, the, if, if it's blah, ate blah, the first blah better be a thing that eats things. And there's a certain category of things that eat things and, you know, that's like animals and people and so on. And, uh, and so that's sort of part of the construction kit.

• 42:52Speaker 01

  So you end up with this kind of notion of a kind of semantic grammar of a, a, a way, a construction kit of how you put words together. My guess is that's essentially what chat G P T has discovered. And, uh, once we understand that more clearly, we'll probably be able to build things like chat G P T much more simply than it's very indirect way to do it, to have this neuron that and keep bashing it and say, you know, make it, you know, predict words better and so on. There's probably a more direct way to do the same kind of thing, but that's, that's sort of what's, what's happened. And this, this moment when it becomes sort of human level performance. You know, very hard to predict when that will happen. It was, it happened, it's happened for things like, well, it happened for kind of visual object

• 43:36Speaker 01

  recognition around 2011, 2012 type timeframe. Um, it's, uh, it's hard to know when these things are going to happen for different kinds of human activities. And I think, uh, but the thing to realize is there are human-like activities and then there are things that we have formalized where we've used, you know, math, we've used other kinds of things as a way to kind of work things out systematically. And that's a different kind of direction than the direction that things like neural nets are going in. And that's, you know, happens to be the direction that I've spent a good part of my life trying to, trying to build up. And these things are very kind of complementary in the sense that.

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• 47:25Speaker 01

  The kind of things like the linguistic interface that are made possible by neural nets kind of feed into this kind of precise computation that we can do on that side.

• 47:38Hala Taha?

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• 49:50Speaker 01

  Yeah, I mean, it's it's always a funny thing because we have an internal view of the fact that there's something going on inside for us. We experience the world and so on. Even when we're looking at other people, it's like it's just a guess that, you know, I know what's going on in my mind. It's just some kind of guess what's going on in your mind, so to speak. And we've developed, you know, the big discovery of our species is language, this way of packaging up the thoughts that are happening in my mind and being able to transmit them to you and having you unpack them and make similar thoughts perhaps in your mind, so to speak. So this this idea of of where can you sort of imagine that

• 50:32Speaker 01

  there's a mind that's operating, that's something which we already we it's not obvious, you know, between different people. We kind of always make that assumption. When it comes to other animals, it's like, well, we're not quite sure, but maybe we can kind of tell that the, you know, our cat had some emotional reaction which sort of reminded us of some human emotion and so on. When it comes to our AIs, you know, I think that that increasingly people will have the view that that kind of the AIs are a bit like them. So when you say, well, is this is there a there there? Is there a thing inside? It's like, OK, is there a thing inside another person? It's it's, you know, you say, well, well, but but, you know, but we can tell that the other person is kind of thinking and doing all

• 51:15Speaker 01

  this stuff. Well, if we were to look inside the brain of that other person, all we'd find is a bunch of electrical signals going around. And those add up to something where there's kind of, you know, it we have the assumption that there's a there's a conscious mind there, so to speak. So I think in the thing that is sort of a we have always felt that sort of our thinking and minds and so on are very far away from other things that are happening in the world. I think the thing that we learn from sort of the advance of AI is, well, actually, there's not as much distance between sort of the amazing stuff of our minds and things that are just able to be constructed computationally. I mean,

• 51:58Speaker 01

  one of the things to realize is that something that's come out of a bunch of science that I've done, this whole question of sort of what thinks, what, what, where is there kind of computational stuff going on? And you might say, well, humans do that, maybe our computers do that. Well, actually, nature does that too. When, you know, people will have this thing, you know, the weather has a mind of its own. Well, what does that mean? Well, it it means typically operationally, it means it seems like the weather is acting with free will. We can't predict what it's going to do. But if we say, well, what's going on in the weather? Well, it's a bunch of kind of, you know, fluid dynamics in the atmosphere and this and that and the other.

• 52:41Speaker 01

  And we say, well, how do we compare that with the electrical processes that are going on in our brains? They're both sort of computations that operate according to certain rules. The ones in our brains, we're kind of familiar with. The ones in the weather, we're not familiar with. But in some sense, that both of these cases, there's a a computation going on. And one of the things that kind of was a big piece of bunch of science I've done is this thing called the principle of computational equivalence, which is kind of this this discovery, this idea that if you look at different kinds of systems operating according to different rules, whether it's a brain or the weather, there is a certain, there's a a commonality, there's the same level of computation is achieved by those different kinds of systems.

• 53:26Speaker 01

  And that's that's not obvious. It's it's kind of like you might say, well, I've got the system and it's just a system that's made from physics as opposed to the system that's the result of lots of biological evolution or whatever. Or I've got the system and it just operates according to these very simple rules that I can write down. It's it's kind of the the we've got, you might have thought that the sort of level of computation that will be achieved in those different cases would be very different. The big surprise is that it isn't. It's the same. And that has all kinds of consequences. Like if you say, OK, I've got the system in nature. Let me predict what's going to happen in it. Well, essentially, what you're doing by by saying I'm going to predict what's going to happen is you're somehow setting yourself up as being smarter than the system in nature.

• 54:11Speaker 01

  It will take it all these computational steps to figure out what it does. But you are going to just jump ahead and say this is what's going to happen in the end. Well, the fact that there's this principle of computational equivalence implies this thing I call computational irreducibility, which is this, the realization that there are many systems where to work out what will happen in that system, you have to do kind of an irreducible amount of computational work. And that's a, that's a surprise because we kind of have been used to the idea that kind of science lets us kind of jump ahead and just say, oh, this is what the answer is going to be. And this is kind of showing us from within science, it's showing us that there's a fundamental limitation where we can't do that. And that's, that's important, you know, when it comes to thinking about things like AI.

• 54:56Speaker 01

  When you say things like, well, let's make sure that AIs never do the wrong thing. Well, problem with that is there's this phenomenon of computational irreducibility. The AI is doing what the AI does, it's doing all these computations and so on. We can't know in advance, we can't just jump ahead and say, oh, we know what it's going to do. We are, we are stuck kind of having to follow through these steps. We can try and make an AI where we can always know what it's going to do. Turns out that AI will be too dumb to be a serious AI. And in fact, we already, we see that happening in recent times of people saying, let's make sure they don't do the wrong thing. When you put enough constraints, it can't really do the things that a computational system should be able to do.

• 55:41Speaker 01

  And it, it doesn't really achieve the kind of this, this level of, of capability that you might call sort of real AI, so to speak.

• 55:51Hala Taha?

  Next, I want to talk about how the world is going to change now that AI is here being more adapted by people. It's becoming more commonplace. How's it going to impact jobs? And also, if you can touch on, you know, the risks of AI, like what are the biggest fears that people have around AI?

• 56:09Speaker 01

  Well, I mean, More and more systems in the world will get automated. This has been a story of technology throughout history. You know, AI is is another step in the automation of things. And it's kind of, you know, when things get automated, things humans used to have to do with their own hands, they don't have to do anymore. The the typical pattern of economies, like in the US or something, is, you know, 150 years ago in the US, most people were doing agriculture. You had to do that with your own hands. Then machinery got built that let that be automated. And the people who, you know, it's like, well, then nobody's going to have anything to do.

• 56:49Speaker 01

  Well, it turned out they did have things to do because that very automation enabled a lot of new types of things that people could do. And I think, you know, for example, the thing that, you know, we're doing, the podcasting thing we're doing right now is enabled by sort of the fact that we have video communication and so on. There was a time when all of that automation that has now led to the kind of telecommunications infrastructure we have wasn't there. And there had to be telephone switchboard operators, you know, plugging wires in and so on. And people were saying, oh, gosh, you know, if we if we automate telephone switching, then all those jobs are going to go away. But actually, what happened was, yes, those jobs went away.

• 57:31Speaker 01

  But that automation opened up many other categories of jobs. So that the typical thing that you see, at least historically, is a big category, there's a there's a big chunk of jobs that are something that people have to do for themselves. That gets automated and that enables what becomes many different possible things that you end up being able to do. And I think the way to think about this is is really the following, that that when, once you've defined an objective, you can build automation that does that objective. Maybe it takes 100 years to get to that automation, but you can, in principle, do that. But then you have the question, well, what are you going to do next? What what are the new things you could do?

• 58:12Speaker 01

  Well, that that question, there are an infinite number of new things you could do. The the, you know, the AI left to its own devices, there's an infinite set of things that it could be doing. The question is, which things do we choose to do? And that's something that is really a matter for us humans. Because it's like, it's it's like you could compute anything you want to compute. And in fact, some part of my life has been exploring kind of the the science of the computational universe. What's out there that you can compute? And the thing that's a little bit kind of sobering is to realize of all the things that are out there to compute. The set that we humans have cared about so far in the developments of our civilization is a tiny, tiny, tiny slice.

• 58:55Speaker 01

  Like, and so that's the uh and and this question of sort of how do we, where do we go from here, so to speak? Is well, what other slices which have now been, now they're possible, which things do we want to do? And I think that the typical thing you see is that a lot of new jobs get created around the things which are still sort of a matter of human choice, what you do. Eventually, it kind of gets standardized and then it gets automated. And then you go on to to another stage. So I I think that the the spectrum of what jobs um sort of will be automated, you know, one of the things that happened back several years ago now.

• 59:34Speaker 01

  The people were saying, oh, machine learning, kind of the the sort of underlying area that leads to sort of neural nets and AI and things like this. You know, machine learning is going to put all these people out of jobs. The thing that was sort of amusing to me was that I knew perfectly well that the first category of jobs that would be impacted were machine learning engineers, because machine learning can be used to automate machine learning, so to speak. And so it was, you know, what what things disappear, what things uh it's not, it's not quite as uh, it's not something where, oh, uh, kind of, well, there's, there's a lot of, you know, once the thing becomes routine, then it can be automated.

• 1:00:17Speaker 01

  And and for example, a lot of people learnt to do programming, low level programming. You know, I've spent a large part of my life trying to automate low-level programming. So in other words, the computational language we've built, which, you know, people like, "Oh, my gosh, I can do this. You know, I can, I can get the computer to do this thing for me by spending an hour of my time. If I were writing kind of standard programming language code, I'd spend a month trying to set my computer up to do this." That's kind of the, the thing we've already achieved is to be able to automate out those kinds of things. What you realize when you automate out something like that is people say, "Oh, my gosh, things have become so difficult now."

• 1:01:00Speaker 01

  Because if you're doing kind of low-level programming, some part of what you're doing is just routine work. You don't have to think that much. It's just like, "Oh, I turn the crank, I get the next, you know, I show up to work the next day, I, I get this piece of code written." Well, if you've automated out all of that, what you realize is most of what you have to do is figure out, "So what do I want to do next?" And that's where this kind of being able to do real computational thinking comes in. Because that's where it's like, so how do you think about what you're trying to do in computational terms? So you can define what you should do next. And I think that's, that, that's an example of, you know, the low-level kind of turn-the-crank programming. I mean, that should be extinct already because, because, you know,

• 1:01:45Speaker 01

  we've spent, I've spent the last forty years trying to automate that stuff. And in some segments of the world, it is kind of extinct because we, we, we did automate it. But there's an awful lot of people where that, they said, "Oh, we can get a good job by learning, you know, C code, C program, C++ programming, or Python, or Java, or something like this." That's, that's what we'll, you know, that's a thing that we can spend our human time doing. It's not necessary. And, and that's kind of, that, that's being more emphasized at this point. The thing that is still very much the human thing is, "So what do you want to do next?", so to speak.

• 1:02:22Hala Taha?

  Hmm. This is so interesting because it's a good story, you know, because you're not saying, "Hey..." We're doomed, you're saying AI is going to actually create more jobs, it's going to automate the things that are repetitive and the things that we still need to make decisions on or decide the direction that we want to go in, that's what humans are going to be doing, sort of shaping all of it. But do you feel that AI is going to like supersede us in intelligence and have this apex intelligence one day where we are not in control of the next thing?

• 1:02:54Speaker 01

  Right, well, so I, you know, mentioned the fact that lots of things in nature compute. Our brains do computation, the weather does computation, the weather is doing a lot more computation than our brains are doing. So if you say what's the apex intelligence in the world, already nature has vastly more computation going on than happens to occur in our brains. The computation going on in our brains is computation where we say, oh, we understand what that is and we really care about that. Whereas the computation that goes on in the, in the, you know, babbling brook or something, we say, well, that's just some flow of water and things, we don't really care about that. So, so we already lost that, that competition of are we the most computationally sophisticated things

• 1:03:38Speaker 01

  in the world? We're not. I mean, we're all, many, many things are equivalent in their computational abilities. So then the question is, well, what will it feel like when AI gets to the point where routinely it's sort of doing, doing all sorts of computation beyond what, what we manage to do? I think it feels pretty much like what it feels like to live in the natural world. The natural world does all kinds of things. There are, you know, occasionally a tornado will happen, occasionally this will happen, we can kind of make some prediction about what's going to happen, but we don't know for sure what's going to happen, when it's going to happen and so on. And that's kind of, I think, what it will sort of feel like to be in a world where most things are run with AI. And we'll

• 1:04:20Speaker 01

  be able to do some science of the AI, saying, just like we can do science of the natural world and say, this is what we think is going to happen. But there's going to be this kind of infrastructure of kind of AI society, there already is to some extent, but that will grow, of more and more things that are sort of happening automatically as a sort of computational process. But in a sense, that's no different from what happens in the natural world. The natural world is just automatically doing things that are not, where we can try and divert what it does, but it's just doing what it does, so to speak. You know, it's, it's, for me, it's, it's kind of one of the things I've long been interested in is kind of, how is the universe actually put together? If we kind of drill down and look at the sort of smallest scales of, of physics and so on, what's, what's down there?

• 1:05:05Speaker 01

  And what we've discovered in the last few years is that it looks like we really can understand the whole of what happens in the universe as a computational process that sort of underneath them, people kind of, people have been arguing for a couple of thousand years whether the world is made of sort of continuous things, whether it's made of little discrete things like atoms and so on. And about a bit more than a hundred years ago, it got nailed down, matter is made of discrete stuff. There are individual atoms and molecules and so on, then light is made of discrete stuff, photons and so on. Space, people had still assumed was somehow continuous, was not made of discrete stuff. And the thing we kind of, well, kind of nailed down, I think in, in

• 1:05:50Speaker 01

  twenty twenty was the idea that space really is made of discrete things. There are discrete elements, discrete kind of atoms of space. And we can really think of, the universe is made of a sort of giant network of atoms of space. And we, we, hopefully in the next few years, maybe if we're lucky, we'll get sort of direct experimental evidence that space is discrete in that way. But one of the things that that makes one realize is it's sort of computation all the way down. At, at the lowest level, the universe consists of this sort of discrete network that keeps on getting updated and it's kind of following these simple rules and so on. It's, it's all rather lovely, but, but that, that kind of makes one, in a sense,

• 1:06:34Speaker 01

  kind of, there's computation everywhere in nature, in our AIs, in our brains. The part that, the, the computation that we care the most about is the part that we with our brains have, and our civilization and our culture and so on, have so far explored. That's the part we care the most about. Over progressively, we should be able to explore more. And we, as, as sort of the computational X fields come into existence and so on, that, that, and we get to use our computers and computational language and so on, we get to kind of, to colonize more of the sort of computational universe. And we get to bring more things into, oh yes, that's the thing we humans talk about. I mean, if you go back

• 1:07:19Speaker 01

  You know, the even just a hundred years. Nobody was talking about all these things that we now take for granted about computers and how they work and and how you can compute things and so on. That was that was just not something within human kind of within our human sphere. Now, the question is, as we go forward with automation, with kind of the formalization of computational language, things like that. What more will sort of be within our kind of human sphere? It's hard to predict. It is to some extent a choice. There are things where we could go in this direction, we could go in that direction. These are things we will eventually humanize, so to speak, and they'll be, you know, it's also if you look at the course of human history.

• 1:08:03Speaker 01

  And you say, what did people think was worth doing? A thousand years ago, a lot of things that people think are worth doing today. People absolutely didn't even think about like, you know, a good example perhaps is, you know, walking on a treadmill. That would just seem just completely stupid to somebody from even a few hundred years ago. And you and you kind of, it's like, why would you do that? Well, I want to live a long life. Why do you even want to live a long life? Well, you know, that's because whatever that wasn't, you know, in in the past, that might not even have been thought of as an objective. And then this, this, you know, that there's a sort of whole chain of why are we doing this?

• 1:08:42Speaker 01

  And that chain is is a is a thing of our time, so to speak, and and that will that will change over time. And I think that that the the the the kind of this. I think what is possible in the world will change. What we get to have sort of explored out of the sort of computational universe of all possibilities will change. Uh, there will no doubt be people who, uh, you know, you you ask, you could ask the question, will, uh.

• 1:09:15Speaker 01

  What will be the role of sort of the biological, sort of, you know,

• 1:09:22Speaker 01

  sort of biological intelligence, so to speak, versus all the other things in the world? And as I say, we're already somewhat in that situation. There are things about the natural world that just happen. And some of those things are things that are much more powerful than us. You know, we don't get to sort of stop the earthquakes and so on. There are things that, so we already are in that situation. It's just that the things that we are doing with AI and so on, we happen to be building a layer of that infrastructure that is sort of of our own construction, rather than something which has been there all the time in nature. And so we've kind of gotten used to it.

• 1:10:03Hala Taha?

  This is so mind blowing, honestly. It's so mind blowing, but I love the fact that you seem to have like a positive attitude towards it. Like you're not, you know, we've had other people on the show that sort of are worried about AI, but you don't have that attitude towards it. It seems like you're more accepting of the fact that this is, it's coming whether we like it or not. Right? And to your point, we're already living in nature, which is way more intelligent than us anyway. And so maybe this is just an additional layer.

• 1:10:31Speaker 01

  Right, I think, I mean, you know, I'm an optimistic person. That's what happens if you, it's a, I've spent my life doing kind of large projects and building big things. You don't, you don't do that unless you have a certain degree of optimism. But I think also that, you know, in the, in the, what will always be the case as, as things change, things that people have been doing will stop making sense. You know, you see this in the intellectual sphere with sort of paradigms in science. It's like, well, somebody, you know, I mean, I built some new things in science where people at first say, oh, my gosh, this is terrible. I've been doing this other thing for 50 years.

• 1:11:16Speaker 01

  You know, I don't want to learn this new stuff. This is, this is, you know, this is a terrible thing. And I think you see that in, you know, there's a, there's a lot in the world where people are like, it's good the way it is, let's not change it. Well, you know, the, the, what's, what's happening is, In the sphere of ideas and in the sphere of technology, sort of things change. And I think to say, you know, the the only things that, you know, if you ask, is it going to wipe our species out? I don't think so. But that would be a that would be a thing that we would probably think is definitively bad.

• 1:11:56Speaker 01

  If we say, well, you know, I spent a lot of time learning how to do, I don't know, write, I don't know, I became, you know, a great programmer in some low level programming language. And by golly, that's not a relevant skill anymore. Um, you know, yes, that can happen. I mean, for example, in in my life, I I got interested in physics when I was pretty young and, you know, when you do physics, you end up having to do lots of mathematical calculations. I never liked doing those things. I always thought, but there were other people who were like, that's what they're into, that's what they like doing. I never liked doing those things. So I taught computers to do them for me. And me plus a computer did pretty well at doing those things. But it's kind of one automated that away.

• 1:12:38Speaker 01

  And that might have been seen as, to me, that was a big positive, because it let me do a lot more. Let me kind of take what I was thinking about and get the sort of superpower to to figure out, to to go go places with that. To other people, that's like, oh, my gosh, the thing that we really were good at doing of doing all these kind of mathematical calculations by hand and so on, that just got automated away. You know, the thing that we like to do isn't a thing anymore. So, you know, that that's a dynamic that I think I think continues. But, you know, having said that, there are plenty of of ridiculous things that sort of get made possible by, you know, whenever there's powerful technology, you can do ridiculous things with it.

• 1:13:22Speaker 01

  And, you know, the the question of exactly what kind of, you know, what what terrible scam will be made possible by what piece of AI, that's that's always a bit hard to predict. It's a kind of a computational irreducibility story of of kind of this this kind of thing of what will people figure out how to do, what will the computers let them do and so on. But I'm, yes, in in general terms, it is my nature to be optimistic, but I think also there is a there is kind of an optimistic path through sort of the way the world is changing, so to speak.

• 1:13:57Hala Taha?

  Yeah, well, it's really exciting. I can't wait to have you back on, maybe in a year, to hear all the other exciting updates that have happened with AI. I end my show asking two questions. Now, you don't have to use the topic of today's episode, you can just use your life experience to answer these questions. So one is, and you can be very quick, what is one actionable thing our young improfitters can do today to become more profitable tomorrow? And this is not just about money, but profiting in life.

• 1:14:24Speaker 01

  I mean, understand computational thinking. This is the, this is the coming paradigm of the 21st century. And if you understand that well, it gives you a huge advantage. And if you say, you know, unfortunately, it's not like you go sign up for a computer science class and you'll learn that. Unfortunately, kind of the educational resources for learning about computational thinking aren't really fully there yet. And it's, it's something which frustratingly, after many years, I've decided I have to, I have to really build much more of these things because other people aren't doing it. And it'll be another, you know, decades before, before it gets done otherwise. But, but yes, learn, learn sort of computational thinking, learn, learn the tools that are

• 1:15:08Speaker 01

  around that. That's a, that's a kind of a, a, a, a quick, a quick way to kind of jump ahead in, in, in whatever you're doing, because there's kind of a, as you make it computational, you get to think more clearly about it and you get the computer to help you kind of jump forward.

• 1:15:27Hala Taha?

  And where can people get resources from you to learn more about that? What, what, where do you recommend?

• 1:15:32Speaker 01

  Well, I mean, the, the, our kind of computational language, Wolfram Language is kind of the main example of sort of where you get to do computational thinking. There's a book I wrote a few years ago called Elementary Introduction to Wolfram Language, which is pretty accessible to people. But hopefully, in another, well, certainly within a year, there should exist a thing that I'm working on right now, which is kind of a directly an introduction to computational thinking, but you'll find a bunch of resources around Wolfram Language that kind of, sort of explain more, although I don't think quite in the right way yet, kind of how one can think about things computationally. That's kind of the, the raw material for doing that.

• 1:16:16Hala Taha?

  Okay. Well, whatever links that we find, I'll stick them in the show notes. And next time, if you have something and you're releasing it, make sure that you contact us so you can come back on Young and Profiting Podcast. Stephen, thank you so much for your time. We really enjoyed having you on Young and Profiting Podcast. Thanks.